#!/bin/bash

################基础配置参数，需要模型审视修改##################
# 必选字段(必须在此处定义的参数): Network batch_size resume RANK_SIZE
# 网络名称，同目录名称
Network="PnasNet5Large"
# 训练batch_size
batch_size=32
# 训练使用的npu卡数
export RANK_SIZE=1
# checkpoint文件路径,以实际路径为准
resume=/home/checkpoint.pth.tar

# 数据集路径,保持为空,不需要修改
data_path=""

# 指定训练所使用的npu device卡id
device_id=0
# 训练epoch
train_epochs=91
# 学习率
learning_rate=0.4
# 加载数据进程数
workers=16


# 参数校验，data_path为必传参数，其他参数的增删由模型自身决定；此处新增参数需在上面有定义并赋值
for para in $*
do
    if [[ $para == --workers* ]];then
        workers=`echo ${para#*=}`
    elif [[ $para == --data_path* ]];then
        data_path=`echo ${para#*=}`
    fi
done

# 校验是否传入data_path,不需要修改
if [[ $data_path == "" ]];then
    echo "[Error] para \"data_path\" must be confing"
    exit 1
fi


###############指定训练脚本执行路径###############
# cd到与test文件夹同层级目录下执行脚本，提高兼容性；test_path_dir为包含test文件夹的路径
cur_path=`pwd`
cur_path_last_diename=${cur_path##*/}
if [ x"${cur_path_last_diename}" == x"test" ];then
    test_path_dir=${cur_path}
    cd ..
    cur_path=`pwd`
else
    test_path_dir=${cur_path}/test
fi


#################创建日志输出目录，不需要修改#################
ASCEND_DEVICE_ID=0
if [ -d ${test_path_dir}/output/${ASCEND_DEVICE_ID} ];then
    rm -rf ${test_path_dir}/output/${ASCEND_DEVICE_ID}
    mkdir -p ${test_path_dir}/output/$ASCEND_DEVICE_ID
else
    mkdir -p ${test_path_dir}/output/$ASCEND_DEVICE_ID
fi


#################启动训练脚本#################
#训练开始时间，不需要修改
start_time=$(date +%s)
# 非平台场景时source 环境变量
check_etp_flag=`env | grep etp_running_flag`
etp_flag=`echo ${check_etp_flag#*=}`
if [ x"${etp_flag}" != x"true" ];then
    source ${test_path_dir}/env_npu.sh
fi

pip3 install -r ${cur_path}/requirements.txt

for((RANK_ID=$device_id;RANK_ID<$((device_id+RANK_SIZE));RANK_ID++));
do

KERNEL_NUM=$(nproc)
PID_START=$((KERNEL_NUM * RANK_ID))
PID_END=$((PID_START + KERNEL_NUM - 1))

nohup \
taskset -c $PID_START-$PID_END python3 -u ./imagenet_fast.py \
    --data ${data_path} \
    --resume ${resume} \
    --start-epoch 90 \
    --max_step 1000 \
    --epochs ${train_epochs} \
    --wd 4e-5 \
    --gamma 0.97 \
    -c ${test_path_dir}/output/${ASCEND_DEVICE_ID}/ \
    -log train_${ASCEND_DEVICE_ID}.log \
    --world_size ${RANK_SIZE} \
    --train-batch ${batch_size} \
    --test-batch ${batch_size} \
    --opt-level O2 \
    --wd-all \
    --warmup 0 \
    --workers ${workers} \
    --lr ${learning_rate} \
    --print-freq 10 \
    --loss_scale 32 \
    --use_aux \
    --local_rank $RANK_ID >> ${test_path_dir}/output/${ASCEND_DEVICE_ID}/nohup.out & \
done

wait


##################获取训练数据################
# 训练结束时间，不需要修改
end_time=$(date +%s)
e2e_time=$(( $end_time - $start_time ))

#结果打印，不需要修改
echo "------------------ Final result ------------------"

# 输出训练精度,需要模型审视修改
train_accuracy=`tail -n 1 ${test_path_dir}/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | awk '{print $5}'`
#打印，不需要修改
echo "Final Train Accuracy : ${train_accuracy}"
echo "E2E Training Duration sec : $e2e_time"

# 训练用例信息，不需要修改
BatchSize=${batch_size}
DeviceType=`uname -m`
CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'acc'

#从train_$ASCEND_DEVICE_ID.log提取Loss到train_${CaseName}_loss.txt中，需要根据模型审视
tail -n 1 ${test_path_dir}/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log | awk '{print $3}' >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt

#最后一个迭代loss值，不需要修改
ActualLoss=`awk 'END {print}'  ${test_path_dir}/output/$ASCEND_DEVICE_ID/train_${CaseName}_loss.txt`

#关键信息打印到${CaseName}.log中，不需要修改
echo "Network = ${Network}" >  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "RankSize = ${RANK_SIZE}" >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "BatchSize = ${BatchSize}" >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "DeviceType = ${DeviceType}" >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "CaseName = ${CaseName}" >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "TrainAccuracy = ${train_accuracy}" >> ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "ActualLoss = ${ActualLoss}" >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
echo "E2ETrainingTime = ${e2e_time}" >>  ${test_path_dir}/output/$ASCEND_DEVICE_ID/${CaseName}.log
